The majority of artificial intelligence research, as it relates from which to
biological senses has been focused on vision. The recent explosion of machine
learning and in particular, dee p learning, can be partially attributed to the
release of high quality data sets for algorithm s from which to model the world
on. Thus, most of these datasets are comprised of images. We believe that
focusing on sensorimotor systems and tactile feedback will create algorithms
that better mimic human intelligence. Here we present SenseNet: a collection of
tactile simulators and a large scale dataset of 3D objects for manipulation.
SenseNet was created for the purpose of researching and training Artificial
Intelligences (AIs) to interact with the environment via sensorimotor neural
systems and tactile feedback. We aim to accelerate that same explosion in image
processing, but for the domain of tactile feedback and sensorimotor research.
We hope that SenseNet can offer researchers in both the machine learning and
computational neuroscience communities brand new opportunities and avenues to
explore.

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